Merge pull request #125 from pymc-labs/isort

fix notebook formatting

Author: drbenvincent

.pre-commit-config.yaml

@@ -10,12 +10,8 @@ repos:
             exclude_types: [svg]
           - id: check-yaml
           - id: check-added-large-files
-    - repo: https://github.com/asottile/seed-isort-config
-      rev: v2.2.0
-      hooks:
-          - id: seed-isort-config
-    - repo: https://github.com/pre-commit/mirrors-isort
-      rev: v5.10.1
+    - repo: https://github.com/pycqa/isort
+      rev: 5.11.2
       hooks:
           - id: isort
             args: [--profile, black]
@@ -24,8 +20,16 @@ repos:
       rev: 22.10.0
       hooks:
           - id: black
-          - id: black-jupyter
     - repo: https://github.com/pycqa/flake8
       rev: 3.9.2
       hooks:
           - id: flake8
+    - repo: https://github.com/nbQA-dev/nbQA
+      rev: 1.5.3
+      hooks:
+        - id: nbqa-black
+          # additional_dependencies: [jupytext]  # optional, only if you're using Jupytext
+        - id: nbqa-pyupgrade
+          args: ["--py37-plus"]
+        - id: nbqa-isort
+          args: ["--float-to-top"]

Makefile

@@ -13,6 +13,8 @@ check_lint:
 	flake8 .
 	isort --check-only .
 	black --diff --check --fast .
+	nbqa black --check .
+	nbqa isort --check-only .
 
 test:
 	pip install -r requirements-test.txt

docs/notebooks/ancova_pymc.ipynb

@@ -37,9 +37,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import causalpy as cp\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
+    "import seaborn as sns\n",
+    "\n",
+    "import causalpy as cp"
    ]
   },
   {

docs/notebooks/did_pymc.ipynb

@@ -13,6 +13,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import arviz as az\n",
+    "\n",
     "import causalpy as cp"
    ]
   },
@@ -480,8 +482,6 @@
     }
    ],
    "source": [
-    "import arviz as az\n",
-    "\n",
     "ax = az.plot_posterior(result.causal_impact, ref_val=0)\n",
     "ax.set(title=\"Posterior estimate of causal impact\");"
    ]

docs/notebooks/did_pymc_banks.ipynb

@@ -23,6 +23,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import arviz as az\n",
+    "import pandas as pd\n",
+    "\n",
     "import causalpy as cp"
    ]
   },
@@ -255,8 +258,6 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
     "df.reset_index(level=0, inplace=True)\n",
     "df_long = pd.melt(\n",
     "    df,\n",
@@ -630,8 +631,6 @@
     }
    ],
    "source": [
-    "import arviz as az\n",
-    "\n",
     "ax = az.plot_posterior(result.causal_impact, ref_val=0)\n",
     "ax.set(title=\"Posterior estimate of causal impact\");"
    ]

docs/notebooks/did_skl.ipynb

@@ -13,6 +13,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import xarray as xr\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
     "import causalpy as cp"
    ]
   },
@@ -31,9 +34,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "\n",
     "result = cp.skl_experiments.DifferenceInDifferences(\n",
     "    data,\n",
     "    formula=\"y ~ 1 + group + t + treated:group\",\n",
@@ -80,7 +80,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.6 ('CausalPy')",
+   "display_name": "CausalPy",
    "language": "python",
    "name": "python3"
   },
@@ -94,12 +94,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:38:29) [Clang 13.0.1 ]"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "46d31859cc45aa26a1223a391e7cf3023d69984b498bed11e66c690302b7e251"
+    "hash": "02f5385db19eab57520277c5168790c7855381ee953bdbb5c89c321e1f17586e"
    }
   }
  },

docs/notebooks/its_covid.ipynb

@@ -13,9 +13,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import causalpy as cp\n",
+    "import arviz as az\n",
     "import pandas as pd\n",
-    "import arviz as az"
+    "\n",
+    "import causalpy as cp"
    ]
   },
   {

docs/notebooks/rd_pymc_drinking.ipynb

@@ -16,20 +16,16 @@
    "outputs": [],
    "source": [
     "# for use when developing the code\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import causalpy as cp\n",
+    "\n",
     "%load_ext autoreload\n",
     "%autoreload 2\n",
     "# %config InlineBackend.figure_format = 'svg'"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import causalpy as cp"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -448,10 +444,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import arviz as az\n",
-    "\n",
-    "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(10, 3))\n",
     "\n",
     "az.plot_forest(result.prediction_model.idata.posterior, var_names=\"beta\", ax=ax[0])\n",
@@ -1135,8 +1127,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(10, 3))\n",
     "\n",
     "az.plot_forest(result3.prediction_model.idata.posterior, var_names=\"beta\", ax=ax[0])\n",

docs/notebooks/rd_skl.ipynb

@@ -13,6 +13,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
+    "from sklearn.gaussian_process.kernels import ExpSineSquared, WhiteKernel\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
     "import causalpy as cp"
    ]
   },
@@ -121,8 +125,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
     "result = cp.skl_experiments.RegressionDiscontinuity(\n",
     "    data,\n",
     "    formula=\"y ~ 1 + x + treated\",\n",
@@ -205,9 +207,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
-    "from sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared\n",
-    "\n",
     "kernel = 1.0 * ExpSineSquared(1.0, 5.0) + WhiteKernel(1e-1)\n",
     "result = cp.skl_experiments.RegressionDiscontinuity(\n",
     "    data,\n",
@@ -250,8 +249,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
     "# use basis splines\n",
     "result = cp.skl_experiments.RegressionDiscontinuity(\n",
     "    data,\n",

docs/notebooks/rd_skl_drinking.ipynb

@@ -17,6 +17,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
     "import causalpy as cp"
    ]
   },
@@ -40,8 +42,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
     "result = cp.skl_experiments.RegressionDiscontinuity(\n",
     "    df,\n",
     "    formula=\"all ~ 1 + age + treated\",\n",